0
research-article

State-Space Model and Kalman Filter Gain Identification by a Kalman Filter of a Kalman Filter

[+] Author and Article Information
Minh Q. Phan

Thayer School of Engineering, Dartmouth College, Hanover, NH 03755
mqphan@dartmouth.edu

Francesco Vicario

Department of Acute Care Solutions, Philips Research, Cambridge, MA 02141
francesco.vicario@philips.com

Richard W. Longman

Department of Mechanical Engineering, Columbia University, New York, NY 10027
rwl4@columbia.edu

Raimondo Betti

Department of Civil Engineering and Engineering Mechanics, Columbia University, New York, NY 10027
betti@civil.columbia.edu

1Corresponding author.

ASME doi:10.1115/1.4037778 History: Received February 12, 2017; Revised June 23, 2017

Abstract

This paper describes an algorithm that identifies a state-space model and an associated steady-state Kalman filter gain from noise-corrupted input-output data. The model structure involves two Kalman filters where a second Kalman filter accounts for the error in the estimated residual of the first Kalman filter. Both Kalman filter gains and the system state-space model are identified simultaneously. Knowledge of the noise covariances is not required.

Copyright (c) 2017 by ASME
Your Session has timed out. Please sign back in to continue.

References

Figures

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In